Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
geom_text(aes(label=country),hjust=1, vjust=2)
We see an interesting spread with an outlier to the right. Answer the following questions, please:
Q1. Why does it make sense to have a log10 scale on x axis? As the gdpPercap (what ever that stands for) Grows logaritmicly and viualisations therefore become clearer when we scale them
Q2. What country is the richest in 1952 (far right on x axis)?
filter(gapminder, year == 1952) %>%
arrange(gdpPercap) %>%
tail()
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 1952 72.7 3327728 10095.
## 2 New Zealand Oceania 1952 69.4 1994794 10557.
## 3 Canada Americas 1952 68.8 14785584 11367.
## 4 United States Americas 1952 68.4 157553000 13990.
## 5 Switzerland Europe 1952 69.6 4815000 14734.
## 6 Kuwait Asia 1952 55.6 160000 108382.
A country with a very small population size - Kuwait
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ylab("Life Expectancy") +
xlab("GDP per capita") +
geom_text(aes(label=country),hjust=1, vjust=2)
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Q3. Can you differentiate the continents by color and fix the axis labels? I tried, but as there is no discribtion of what the variable “gdpPercap” is, it’s hard to know what is stands for, but I’m just guessing here
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
ylab("Life Expectancy") +
xlab("GDP per capita") +
geom_text(aes(label=country),hjust=1, vjust=2)
Q4. What are the five richest countries in the world in 2007?
filter(gapminder, year == 2007) %>%
arrange(gdpPercap) %>%
tail()
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 2 Ireland Europe 2007 78.9 4109086 40676.
## 3 United States Americas 2007 78.2 301139947 42952.
## 4 Singapore Asia 2007 80.0 4553009 47143.
## 5 Kuwait Asia 2007 77.6 2505559 47307.
## 6 Norway Europe 2007 80.2 4627926 49357.
Norway, Kuwait, Singapore, the US, Ireland
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
anim1 <- anim + transition_states(year,
transition_length = 1,
state_length = 1)
anim1
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively] See chunk below
Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]
options(scipen=10000) #should remove scientific notation and answer Q6 - i think scipen = just has to be a high enough number
anim1_1 <- anim1 + labs(x = "GDP per capita", y = "Life expectancy") + transition_time(year) +
labs(title = "Year: {frame_time}")
anim1_1
anim2_1 <- anim2 + labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
ease_aes('linear')
anim2_1
Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]
What has changed between my mom and I was born (1960 vs 1998) –> closets values in the data is 1962 vs1997
p_60 <- ggplot(subset(gapminder, year == 1962), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
ylab("Life Expectancy") +
xlab("GDP per capita")
p_98 <- ggplot(subset(gapminder, year == 1997), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10() +
ylab("Life Expectancy") +
xlab("GDP per capita")
plot_grid(p_60, p_98, labels = c('1962', '1997'))
Overall, all countires have improved health and wealth over the time period. The plot suggests that Asia have inproved in both health and wealth together with the american countries. Also, some areas of africa seems to have increased more than average as well.